Rapid development of virtual personal assistant applications

ABSTRACT

A platform for developing a virtual personal assistant (“VPA”) application includes an ontology that defines a computerized structure for representing knowledge relating to one or more domains. A domain may refer to a category of information and/or activities in relation to which the VPA application may engage in a conversational natural language dialog with a computing device user. Re-usable VPA components may be linked to or included in the ontology. An ontology populating agent may at least partially automate the process of populating the ontology with domain-specific information. The re-usable VPA components may be linked with the domain-specific information through the ontology. A VPA application created with the platform may include domain-adapted re-usable VPA components that may be called upon by an executable VPA engine to determine a likely intended meaning of conversational natural language input of the user and/or initiate an appropriate system response to the input.

BACKGROUND

Computerized systems commonly known as virtual personal assistants(“VPAs”) can interact with computing device users in a conversationalmanner. To do this, the VPA needs to be able to correctly interpretconversational user input, execute a task on the user's behalf,determine an appropriate response to the input, and present the responsein a way that the user can readily understand and appreciate as beingresponsive to the input. A complex assortment of software componentswork together to accomplish these functions. Further, even veryapplication-specific VPAs typically need to access and reason over alarge amount of knowledge. Such knowledge includes information aboutthose aspects of the world that the computing device user may wish todiscuss with the VPA, the manner in which humans normally talk aboutthose aspects of the world, and the applicable cultural norms,activities, and human behaviors. As a result, developing a VPAapplication traditionally has been an arduous task.

BRIEF DESCRIPTION OF THE DRAWINGS

This disclosure is illustrated by way of example and not by way oflimitation in the accompanying figures. The figures may, alone or incombination, illustrate one or more embodiments of the disclosure.Elements illustrated in the figures are not necessarily drawn to scale.Reference labels may be repeated among the figures to indicatecorresponding or analogous elements.

FIG. 1 is a simplified module diagram of at least one embodiment of avirtual personal assistant (“VPA”) development platform embodied in acomputing system as disclosed herein;

FIG. 2 is a simplified module diagram of at least one embodiment of aVPA application that may created using the platform of FIG. 1, embodiedin a computing system;

FIG. 3 is a simplified module diagram of at least one embodiment of theontology populating agent of FIG. 1;

FIG. 4 is a simplified module diagram of at least one embodiment of thelocal model creator of FIG. 3;

FIG. 5 is a simplified flow diagram of at least one embodiment of amethod for populating a domain ontology as disclosed herein;

FIG. 6 is a simplified graphical representation of at least oneembodiment of a hierarchical domain ontology as disclosed herein;

FIG. 7 is a simplified graphical representation of at least oneembodiment of aspects of a VPA model for an e-commerce domain, which maybe created using the platform of FIG. 1;

FIG. 8 is a simplified graphical representation of at least oneembodiment of additional aspects of the VPA model of FIG. 7;

FIG. 9 is a simplified flow diagram of at least one embodiment of amethod for developing a VPA application using the platform of FIG. 1;

FIG. 10 is a simplified illustration of at least one embodiment of agraphical user interface for the platform of FIG. 1; and

FIG. 11 is a simplified block diagram of at least one embodiment of ahardware environment for the platform of FIG. 1 and/or the VPAapplication of FIG. 2.

DETAILED DESCRIPTION OF THE DRAWINGS

While the concepts of the present disclosure are susceptible to variousmodifications and alternative forms, specific embodiments thereof areshown by way of example in the drawings and are described in detailbelow. It should be understood that there is no intent to limit theconcepts of the present disclosure to the particular forms disclosed. Onthe contrary, the intent is to cover all modifications, equivalents, andalternatives consistent with the present disclosure and the appendedclaims.

As disclosed herein, the creation of natural language (“NL”) dialogcomputer applications using virtual personal assistant (“VPA”)technology can be facilitated with the use of an ontology. The ontologymay be tailored to the particular needs of VPA applications (as opposedto other types of interactive software applications), in at least somerespects. As shown in FIG. 2, described below, a VPA application can be“modularized” in the sense that certain core functional components,which may be referred to herein collectively as the “VPA engine,” can beseparated from other components that “feed” the VPA engine. Such othercomponents supply the VPA engine with the information and logic it needsto determine a computing device user's intended goal or objective inusing the VPA, or to locate and retrieve pertinent information, providea service, or initiate or execute some other action with the computingdevice, all the while interacting with the user in a more or lesshumanlike, conversational manner. The ontology may be designed tofacilitate the selection, development and/or configuring of these“feeder” components of the VPA application as needed to achieve thedesired VPA functionality for a particular domain or domains.

In some cases, the ontology may be defined at a general level, e.g., asa shared or “shareable” ontology that can be used to develop VPAapplications for multiple different domains. With the tools andtechniques described herein, the shareable ontology can be adapted to aspecific domain using knowledge that is readily available, such as datathat is posted on Internet web pages. Such knowledge can be extractedfrom web pages or other data sources, and linked with the ontology in anautomated way, as described below. Moreover, as disclosed herein, theontology can be used to automate the selection and instantiation ofgeneral-purpose or “default” VPA “feeder” components, such as NL dialogsand task flows, in a form that is appropriate for a specific domain. Inthese and other ways, the development and maintenance of VPAapplications can be simplified and accelerated.

Referring now to FIG. 1, an embodiment of a VPA development platform 110that can be used to more rapidly create, support, and/or maintain VPAapplications includes a shareable ontology 112, a number of re-usableVPA components 114, a domain knowledge base 116, an ontology populatingagent 118, an ontology visualization module 120, and an inheritancereasoning module 122. The VPA development platform 110 and its variouscomponents are embodied as computer software, firmware, and/or hardwarein a computing system 100. The computing system 100 may include one ormultiple computing devices as described more fully below with referenceto FIG. 11.

The ontology 112 is embodied as a computerized knowledge representationframework. The illustrative shareable ontology 112 is embodied as a“general-purpose” or “shared” ontology that can be used to develop VPAapplications for one domain or for many different domains. That is, theshareable ontology 112 defines a computerized structure for representingknowledge that relates to one domain or multiple domains. Such astructure includes ontological concepts (or “objects”), properties (or“attributes”) that are associated with the concepts, and datarelationships between or among the ontological concepts and properties.For example, in an ontology for a general-purpose “retail” domain,“product” may be an ontological concept, “color,” “description,” and“size” might be properties of the “product” concept, “has-a” might be atype of data relationship that exists between each of those propertiesand the “product” concept, and “is-a” might be a type of datarelationship that exists between the concept “product” and a sub-concept“shirts.” That is, in the illustrative retail domain, a shirt is type ofproduct that has a color, description, and size.

As discussed further below with reference to FIGS. 6-8, some embodimentsof the ontology 112 are hierarchical, in that the ontology 112 leveragesthe “natural” inheritance structure of knowledge about a particular partof the real world. A hierarchical ontology can enable data and/or VPAcomponents to be shared and re-used through inheritance. In someembodiments, the VPA development platform may include an inheritancereasoning module 122, described below.

As used herein, the term “domain” may refer to a category of informationand/or activities in relation to which a VPA application may engage in aconversational natural language dialog with a computing device user. Insome cases, “domain” may refer to the scope of a particular VPAapplication or a portion thereof. As such, a domain may correspond toone or more ontological concepts and/or properties that are defined inthe ontology 112. For example, a VPA application may be directedspecifically to e-commerce shopping for “oil filters” (a single domainor concept) while another VPA application may be more broadly directedto “automotive supplies” (a broader category of items that may includeoil filters, spark plugs, and other supplies). Embodiments of theontology 112 may be created, updated, and maintained using a knowledgerepresentation language, such as OWL (Web Ontology Language) and/or anontology-authoring mechanism such as RDF (Resource DevelopmentFramework).

The ontology 112 is designed for use in connection with the developmentof VPA applications. As such, the ontology 112 represents knowledge in away that is helpful and useful to the various components of a VPAapplication. To do this, data relationships are established between theelements of the ontology 112 and the re-usable VPA components 114. Morespecifically, the re-usable VPA components 114 are included in or linkto the ontology 112, as discussed in more detail below. Further, theontology 112 may define certain ontological concepts and properties, andrelationships between the concepts and properties, so as to model a wayin which humans are likely to talk about them with the VPA application.Alternatively or in addition, the ontology 112 may define certainontological concepts, properties, and relationships in a way that thatis likely to coincide with the way that information is encountered bythe ontology populating agent 118, described below.

As another example, the ontology 112 may define an inheritancerelationship between an ontological concept of “jeans” and a “pants”concept because many, if not all, of the properties of pants are alsoapplicable to jeans. As such, during the development of a VPAapplication designed for e-commerce shopping for pants, the platform 110may use the ontology 112 to help the developer create a natural language(“NL”) response that relates to pants. Through the ontology 112, theplatform 110 may know that pants have an “inseam” and that the inseam isa measurement that is used to determine a person's pants size.Accordingly, the platform 110 may suggest or otherwise help the VPAdeveloper create an NL response such as “please tell me your inseammeasurement,” and incorporate that NL response into the VPA applicationfor pants shopping. Further, since the platform 110 knows through theontology 112 that jeans are a type of pants, the platform 110 maysuggest or otherwise help the application developer link thealready-created NL response, “please tell me your inseam measurement”with the concept of “jeans.” The platform 110 may “remember” the linkbetween the “inseam” NL response and the concept of jeans (through theontology 112), so that later, if a VPA application is developedspecifically for e-commerce shopping for jeans, the platform 110 cansuggest or otherwise help the VPA developer incorporate the “inseam” NLresponse into the jeans-specific e-commerce VPA application.

In addition to defining data relationships between different ontologicalconcepts and properties, the ontology 112 defines relationships or“links” between the ontological concepts and properties and there-usable VPA components 114. That is, the re-usable VPA components 114are programmatically linked with one or more of the ontological conceptsand/or properties in the ontology 112. In this way, the ontology 112 canbe used to automate or at least partially automate the selection ofre-usable VPA components 114 for use in a VPA application for aparticular domain of interest and to instantiate those selectedcomponents for the domain of interest.

As used herein, terms such as “relation,” “data relationship,”“linkage,” and “link” may refer to a logical association or semanticrelationship that may be implemented in software using specializedcomputer programming statements or constructs. For example, inartificial intelligence-based systems, such statements may be referredto as sentences or axioms (e.g., “pants is-a apparel”, “tool is-a retailproduct”). Other forms of linking mechanisms, such as pointers, keys,references, and/or others may also be used to establish logicalassociations or semantic relationships between elements of the ontology112 or between the ontology 112 and the re-usable components 114. Insome embodiments, the re-usable VPA components 114 may be included inthe ontology 112. For example, the ontology 112 may be viewed as the“union” of the re-usable VPA components 114 and the domain knowledgebase 116, where the re-usable VPA components 114 include a number ofdifferent sub-components as illustrated in FIG. 2. In some embodiments,the re-usable VPA components 114 and/or portions of the domain knowledgebase 116 may be stored in the same container or containers as theontology 112 or portions thereof (where “container” refers generally toa type of computerized data storage mechanism). For example, a VPAcomponent 114 that is an NL grammar related to pants (e.g., “I like thenatural-fit capris”) may be linked with or included in the containerthat corresponds to the “pants” concept of the domain knowledge base 116through the ontology 112. In other embodiments, the re-usable components114 are linked with the domain knowledge base 116 through the ontology112 using, for example, one of the programming mechanisms above (withoutconcern as to where those components 114 may be stored).

The re-usable VPA components 114 include software components, such asdata, logic, alphanumeric text elements, sentences or phrases,variables, parameters, arguments, function calls, routines orprocedures, and/or other components, which are used by the VPAapplication to conduct an NL dialog with a computing device user and/orinitiate or execute a task or activity for the user based on the VPA'sunderstanding of the NL dialog. At a high level, the re-usable VPAcomponents 114 may be categorized as follows: those that assist the VPAapplication in understanding the user's intended meaning, goal, orobjective of his or her NL dialog input, those that help the VPAapplication reason about the user's intended meaning, goal, or objectiveand determine an appropriate system response, and those that generatefor the VPA application output formulated in a suitable fashion giventhe user's intent as previously determined by the VPA application.

Traditionally, many of these types of VPA components have been createdindividually by hand (meaning, manual creation using a computingdevice). As disclosed herein, the VPA components 114 are “re-usable” inthat they are initially defined and created for a general purpose andthen “instantiated” for a given domain with the help of the ontology112, the ontology populating agent 118, and, in some embodiments, theinheritance reasoning module 122. As mentioned above and shown in FIG.2, the components 114 can be considered “feeder” components that servicethe core functional components of the VPA application rather than beinga part of the core functionality itself. In this way, both the corefunctionality or “VPA engine” and the feeder components 114 can beseparated from the domain-specific knowledge that is needed to create aVPA application for a particular domain. That is to say that someembodiments of the platform 110 at least initially provide“domain-independent,” “generic,” or “default” versions of the re-usableVPA components 114 that can be adapted or customized for domain-specificVPA applications. It should be noted however, that terminology such as“general-purpose,” “domain-independent,” “generic,” “default,” or, onthe flip side, “domain-specific” or “domain-dependent,” as used herein,may each refer to any level of abstraction. For example, in somecontexts, “e-commerce” may be considered a “general-purpose” ontologicalconcept or domain while in other contexts, sub-concepts such as“apparel,” or even “pants” may be considered “general-purpose,” and evenmore specific concepts (e.g., “jeans,” “dresses,” etc.) may beconsidered “domain-specific” or “domain-dependent.” As such, the term“re-usable” does not necessarily imply that a particular VPA component114 cannot be domain-specific. In other words, a VPA component 114 canbe “re-usable” even if it is “domain-specific.” For instance, as notedabove, a VPA component 114 that relates to “pants” may be consideredgeneric if the VPA application is specifically directed to “women'spants” but may be considered domain-specific if the VPA application ismore broadly directed to all types of apparel. In either case, the VPAcomponent 114 may be re-used in the sense that it may be adapted for usein a more general domain or a more specific domain, or adapted for usein another domain entirely, depending on the structure of the ontology112.

The domain knowledge base 116 or “domain ontology” is included in orlinked with the overall ontology structure 112 or portions thereof so asto guide the linkages/relationships between or among the re-usable VPAcomponents 114. That is, data objects and attributes that are defined inthe domain knowledge base 116 correspond to concepts, properties, anddata relationships of the ontology 112, so that re-usable components 114that are linked with the ontology 112 can be adapted to the domain(e.g., by replacing parameters with actual domain-specific data values).The illustrative domain knowledge base 116 is embodied as a datastructure or structures (e.g. database(s), table(s), data files, etc.)in which data records and data values corresponding to the variouselements of the ontology 112 may be stored. Once populated (e.g., by theontology populating agent 118), the domain knowledge base 116 may bereferred to as a “populated” ontology or a domain-specific “leaf,”“node,” or “instance” of the ontology 112 (keeping in mind that“domain-specific” may refer to any level of abstraction that may beneeded by a particular VPA application, as discussed above).

As an example, in developing a VPA application for an e-commerce vendorthat sells jeans, an embodiment of the ontology 112 may be defined toinclude “jeans” as an ontological concept having properties of style,color, size, and care instructions. A corresponding embodiment of thedomain knowledge base 116 may have stored therein individual datarecords that each include data values for each style of jeans sold bythe e-commerce vendor, the colors and sizes in which each style isavailable, and the care instructions applicable to each style of jeans.A populated version of the domain knowledge base 116 may contain datavalues such as “boot cut” and “slim,” which map to a “style” property ofa “jeans” concept in the ontology. In this example, “style” may beconsidered a “common characteristic” that links the data values in thedomain knowledge base 116 with the ontology.

The domain knowledge base 116 can be instantiated or populated with datavalues in a number of different ways, including manual data entry,interfacing with the vendor's back-end systems (e.g., via an applicationprogramming interface or API), or with the help of the ontologypopulating agent 118. Once populated with data values, the domainknowledge base 116 can be used to instantiate new or customized versionsof the re-usable VPA components 114. This can be done by virtue of thelinkages between the re-usable VPA components 114 and the ontology 112,and the linkages between the elements of the domain knowledge base 116and the ontology 112. Such linkages are established based oncharacteristics that these elements have in common with each other, asdescribed further below with reference to FIGS. 3 and 4.

The illustrative ontology populating agent 118 is embodied as acomputerized sub-system or module (e.g., software, firmware, hardware,or a combination thereof) that mines, “scrapes” or otherwise obtainsdata from Internet web pages (or other electronic data sources to whichthe agent 118 has access), maps the scraped data to the structure of theontology 112, and populates the domain knowledge base 116. The ontologypopulating agent 118 may be used to develop VPA applications thatsupport transactional web sites, including web pages or web sites thatsupport electronic transactions with computing device users that relateto a domain of interest or to items in a domain (e.g., e-commercetransactions, financial transactions, healthcare-related transactions,and/or others). The ontology populating agent 118 may be used toharvest, from the relevant web page or pages, the applicabledomain-specific information that needs to be applied to or incorporatedinto the re-usable VPA components 114 for a particular application. Insome cases, other types of publicly-available electronic data sourcesmay be mined by the ontology populating agent 118 to bolster the depthand/or breadth of knowledge that can be “fed” to a particular VPAapplication. For instance, competitor web pages or web sites, publiclyavailable product review pages, publicly available dictionaries andknowledge bases (e.g., DICTIONARY.COM, WIKIPEDIA, and/or others), publicareas of social media sites (e.g., FACEBOOK, GOOGLE+, etc.), publiclyavailable blogs, and/or other data sources may be mined to provideadditional information for use by the VPA application. Such informationmay include alternative names, nicknames, synonyms, abbreviations, andthe like, as well as current context information (e.g., in thee-commerce domain, information about competitor products, or items orstyles of products that are currently popular or appear to be a frequenttopic of conversation). The operation of the ontology populating agent118 is described in more detail below with reference to FIGS. 3-5.

The ontology visualization module 120 is embodied as a computerizedsub-system or module (e.g., software, firmware, hardware, or acombination thereof) that presents an interactive representation of theontology 112 and/or the re-usable VPA components 114 to a computingdevice user, such as a VPA application developer or knowledge baseengineer, for the purpose of developing a VPA application. The ontologyvisualization module 120 allows the developer to navigate and explorethe ontology 112, visually. In some embodiments, the visualizationmodule 120 presents a graphical representation of the ontology 112similar to the illustrative depictions shown in FIGS. 6-8. Such avisualization may be simple enough for an end user or another personwithout sophisticated computer programming skills to understand and use.In any case, the ontology visualization module 120 permits the developeror other user to assign concepts and properties to the various elementsand levels of the ontology 112 and thereby define relationships betweenthe concepts and properties. The ontology visualization module 120 maydo so by, for example, allowing the user to “drag and drop” graphicalrepresentations of the ontological concepts and properties from oneelement or level of the ontology 112 to another using, e.g., a computermouse, stylus, or one's finger.

The illustrative ontology visualization module 120 allows the developeror other user to associate the re-usable VPA components 114 with theontology 112 in a similar manner. For example, as indicated by FIGS.6-8, the links between VPA components 114 and their correspondingontological concepts can be presented graphically on a display screen ofthe computing system 100, and the ontology visualization module 120 mayallow such links to be added or changed by “pointing and clicking,”“dragging and dropping,” or other mode of user input. FIG. 10, describedbelow, illustrates a simplified example of a display screen that may bepresented to a computing device user to enable the selection ofre-usable VPA components 114 for inclusion in a VPA application.

In some embodiments, the ontology visualization module 120 includes aninheritance reasoning module 122. The illustrative inheritance reasoningmodule 122 leverages the organizational structure of the ontology 112 toprogrammatically explore and follow data relationships and linkages asneeded for the development of a VPA application. To do this, theinheritance reasoning module 122 analyzes the existing programmaticstatements (e.g., sentences and/or axioms) that define the datarelationships between the concepts and properties in the ontology 112.Such statements may indicate, for example, subsumption relationships inwhich concepts that are defined as sub-classes or sub-categories ofother concepts in the ontology 112 inherit all of the properties andrelations of their respective parent concepts (e.g., a “child” conceptis “subsumed” by its parent).

In many cases, (e.g., where straightforward hierarchical relationshipsare involved) no reasoning algorithms are needed by the inheritancereasoning module 122, or the inheritance reasoning module 122 may beomitted. Where the ontology 112 includes other kinds of relationships(e.g., temporal), however, the inheritance reasoning module 122 mayapply one or more automated reasoning algorithms to reason over apopulated domain knowledge base 116 to infer new data relationshipsand/or linkages based on the existing data relationships and/or linkagesthat are contained in the ontology 112. That is, the inheritancereasoning module 122 may observe that a particular combination of datarelationships and/or linkages exists in the ontology 112 and based onthat observation, add the reasoned-about relationships to the ontology112. Following that, the ontology visualization module 120 may make asuggestion or recommendation to the VPA application developer as to adata relationship or linkage that may be appropriate for a new domain ora new piece of data.

As an example, if the VPA developer is designing a new VPA applicationfor jeans shopping, the developer is using a “general-purpose”e-commerce ontology that defines “apparel” as a sub-class orsub-category of a “purchasable item” concept, and the developer informsthe platform 110 that “jeans” are a type of apparel (or the platform 110learns that relationship in an automated fashion, as described below),the inheritance reasoning module 122 may suggest to the developer thatall or a portion of the re-usable VPA components 114 that are linkedwith the “apparel” concept in the ontology be included in the new VPAapplication for jeans. In some cases, the module 122 or perhaps someother mechanism of the ontology visualization module 120, or theplatform 110 more generally, may simply proceed to establish the newlinkage in an automated fashion without requiring input from the user.In those cases, the ontology visualization module 120 may present adepiction of the new relationship on a display of the computing system100, for example, to let the developer know that the new relationshiphas been created and/or to allow the developer an opportunity to undo ormodify the relationship.

As another example, referring to FIG. 10, if the VPA developer isdeveloping a new VPA application for a specific domain of e-commerce(e.g., jeans), and the developer selects a re-usable component 114 thatis an intent 226 called “buy product,” the inheritance reasoning module122 may infer that the other VPA components 114 that are associated withthe “product” concept in the ontology 112 are also likely to beapplicable to jeans, and also those associated with any “parent”concepts of the product concept (e.g., “retailer,” etc.) that aredefined in the ontology 112. As such, the inheritance reasoning module122 may suggest to the developer all of the other re-usable components114 that are linked with the “buy product” intent (through the “product”concept of the ontology) for inclusion in the new VPA application. Inthis way, the inheritance reasoning module 122 can help automate theselection and instantiation of VPA components for particular domains, sothat there is no need for the VPA developer to create new components114, or create new versions of the components 114, by hand.

Referring now to FIG. 2, a simplified depiction of functional componentsof an embodiment of a VPA application 210, which can be constructed withthe use of the VPA development platform 110, is shown. The VPAapplication 210 is embodied in a computing system 200. The computingsystem 200 typically includes a mobile device, such as a smart phone,tablet computer, e-reader, or body-mounted device (e.g., goggles, wristwatch, or bracelet), but may include other types of computing devices orcombinations of devices as described more fully below with reference toFIG. 11.

The illustrative VPA 210 includes a multi-modal user interface 212, aVPA engine 214, and a number of domain-adapted re-usable components 222.Some examples of VPA applications including multi-modal user interfacesand VPA engine components are described in other patent applications ofSRI International, for example, Tur et al., PCT InternationalApplication Publication No. WO 2011/028833, entitled “Method andApparatus for Tailoring Output of an Intelligent Automated Assistant toa User,” Yadgar et al., U.S. patent application Ser. No. 13/314,965,filed Dec. 18, 2011, entitled “Generic Virtual Personal Assistant,” Nitzet al., U.S. patent application Ser. Nos. 13/585,003 and 13/585,008,filed Aug. 14, 2012, both entitled “Method, System, and Device forInferring a Mobile User's Context and Proactively Providing Assistance,”and Wolverton et al., U.S. patent application Ser. Nos. 13/678,209 and13/678,213, filed Nov. 15, 2012, both entitled “Vehicle PersonalAssistant.” A brief overview of the functionality of the user interface212 and the VPA engine 214 follows. As disclosed herein, in operation,the VPA engine 214 is in bidirectional communication with both the userinterface 212 and the re-usable VPA components 222, as shown, by one ormore communication links such as any of those described herein. Incontrast to conventional VPA applications, the VPA 210 is constructedwith the domain-adapted re-usable components 222, as described herein.

Each of the components 212, 214, 222 and their respective subcomponentsshown in FIG. 2 are embodied as computerized sub-systems, modules,and/or data structures (e.g., software, firmware, hardware, or acombination thereof) that are accessible to and in some cases,executable by, a central processing unit (e.g., a controller orprocessor, such as the processor 1112 of FIG. 11).

The illustrative multi-modal user interface 212 captures conversationalnatural language (“NL”) input of a computing device user, as well asother forms of user input. In some embodiments, the interface 212captures the user's spoken natural language dialog input with amicrophone or other audio input device of the computing system 200.Alternatively or in addition, the interface 212 captures text-basednatural language dialog input by, for example, a touch pad, key pad, ortouch screen of the computing system 200. Other (e.g., non-NL dialog)user inputs also may be captured by a touch pad, key pad, touch screen,or other input device, through the user interface 212. Such inputs mayinclude, for example, mouse clicks, taps, swipes, pinches, and/orothers. In some cases, the interface 212 may capture “off-device” bodymovements or other gesture-type inputs (such as hand waves, head nods,eye movements, etc.) by, e.g., a camera, motion sensor and/or kineticsensor, which may be integrated with or otherwise in communication withthe computing system 200. In any event, the captured user inputs are atleast temporarily stored in memory of the computing system 200. Whilethe VPA 210 is often mainly concerned with processing the NL dialoginputs, any or all of the other forms of user inputs may be used by theVPA 210 to aid in its understanding of the NL dialog inputs, todetermine a suitable response to the NL dialog inputs, or for otherreasons.

While in many cases the conversational NL dialog that occurs between thecomputing device user and the computing system 200 is initiated by userinput, this need not be the case. In some embodiments, the VPA 210 mayoperate in a proactive manner to initiate a natural language dialog withthe user in response to the user's inputs (e.g., non-NL inputs) orsensed information obtained or derived from, for example, location-basedsystems (e.g., global positioning systems or GPS, cellular systems,and/or others). Thus, the user inputs, including the user-generated NLdialog inputs, can include natural language in a dialog initiated by theuser and/or the user's natural language responses to system-generatedoutput. For example, the natural language dialog inputs may includequestions, requests, statements made by the user to begin aninformation-seeking dialog, commands issued by the user to cause thesystem 200 to initiate or undertake some action, responses tosystem-executed actions, and/or responses to questions presented by thesystem 200. A portion of the user interface 212 may convert the humannatural language dialog inputs into machine-readable versions thereof,or this may be done by a component of the VPA engine 214, describedbelow. As noted above, the NL dialog inputs captured and processed bythe user interface 212 may be in the form of audio, text, some othernatural language inputs, or a combination thereof.

The multi-modal user interface 212 is in bidirectional communicationwith the VPA engine 214 by one or more electronic signal paths (e.g., abus, a network connection, or other type of wired or wireless signalpath or paths). The NL inputs and other user inputs captured by themulti-modal user interface 212 are thereby received and processed by theVPA engine 214. In the illustrated example, the VPA engine 214 includesa number of executable software modules such as a user intentinterpreter 216, a reasoner 218, and an output generator 220. Otherembodiments may include additional components or modules, such as aninformation retrieval engine. Further, some components described hereinas being included in the VPA engine 214 may be located external to theVPA 210 in some embodiments, and thus communicate with the VPA 210 by asuitable communication link such as one of the types of communicationlinks described herein.

The user intent interpreter 216 determines a meaning of the user's NLinput that it believes (e.g., has a statistically high degree ofconfidence) most closely matches the user's actual intent or goal of theuser's communication. In the case of spoken NL dialog input, the userintent interpreter 216 (or an external automated speech recognition(ASR) system) converts the user's natural language audio into a text orotherwise machine-readable format that can be used for further analysisperformed by the user intent interpreter 216. The user intentinterpreter 216 may apply syntactic, grammatical, and/or semantic rulesto the NL dialog input, in order to parse and/or annotate the input tobetter understand the user's intended meaning and/or to distill thenatural language input to its significant words (e.g., by removinggrammatical articles or other superfluous language).

As used herein, terms such as “goal,” “objective,” or “intent” are usedto convey that the VPA 210 attempts to determine not only what the wordsof the user's conversational input mean, but the user's actual intendedgoal or objective, which he or she used those words to express. To dothis, the VPA 210 often needs to consider the dialog context and/orother aspects of the user's current context. For example, the user mightsay something like “I'll take it” or “get me that one,” which reallymeans that the user's goal is to buy a certain product, where theproduct may have been identified by the user in a prior round of dialogor identified by the system through other multi-modal inputs (such as atap selecting an on-screen graphic). Determining the user's intendedgoal or objective of a dialog often involves the application ofartificial-intelligence based methods.

Some embodiments of the user intent interpreter 216 may include anautomatic speech recognition (ASR) system and a natural languageunderstanding (NLU) system. In general, an ASR system identifies spokenwords and/or phrases in verbal natural language dialog inputs andrecognizes and converts them into text form (e.g., words, word strings,phrases, “segments,” “chunks,” “sentences,” or other forms of verbalexpression). There are many ASR systems commercially available; oneexample is the DYNASPEAK system, available from SRI International. Ingeneral, an NLU system receives the ASR system's textual hypothesis ofthe user's NL input. Of course, where the user's NL dialog inputs arealready in text form (e.g., if typed using a keypad of the computingsystem 200), the ASR processing may be bypassed.

The NLU system typically parses and semantically analyzes and interpretsthe verbal content of the user's NL dialog inputs that have beenprocessed by the ASR system. In other words, the NLU system analyzes thewords and/or phrases produced by the ASR system and determines themeaning most likely intended by the user given, for example, other wordsor phrases presented by the user during the dialog and/or one or more ofthe VPA components 222. For instance, the NLU system may apply arule-based parser and/or a statistical parser to determine, based on theverbal context, the likely intended meaning of words or phrases thathave multiple possible definitions (e.g., the word “pop” could mean thatsomething has broken, may refer to a carbonated beverage, or may be thenickname of a person, depending on the context, including thesurrounding words and/or phrases of the current dialog input, previousrounds of dialog, and/or other multi-modal inputs. A hybrid parser mayarbitrate between the outputs of the rule-based parser and thestatistical parser to determine which of the outputs has the betterconfidence value. An illustrative example of an NLU component that maybe used in connection with the user intent interpreter 216 is the SRILanguage Modeling Toolkit, available from SRI International.

The user intent interpreter 216 combines the likely intended meaning,goal, and/or objective derived from the user's NL dialog input asanalyzed by the NLU component with any multi-modal inputs andcommunicates that information to the reasoner 218 in the form of a “userintent.” In some embodiments, the user intent is represented as anoun-verb or action-object combination, such as “buy product” or “searchproduct category,” which specifies an activity that the user desires tohave performed by the VPA and an object (e.g., person, place or thing)that is the subject of that activity. Generally speaking, the reasoner218 synthesizes the user intent and/or any of the other available inputsin view of applicable dialog models, business logic, rules, etc. (whichmay be supplied by one or more of the VPA components 222). From thisanalysis, the reasoner 218 determines a likely appropriate task toexecute on the user's behalf and/or a likely appropriate system responseto the user's intended goal or objective as derived from the meaning ofthe inputs and reflected in the user intent (where “likely appropriate”may refer to a computed statistical measure of confidence determinedand/or evaluated by the reasoner 218). In some cases, the likelyappropriate system task or response may be to ask the user foradditional information, while in other cases, the likely appropriatesystem task or response may involve building a search query based on theinputs and execute an information retrieval process, or to execute someother piece of computer program logic (e.g., to launch an externalsoftware application or follow a link to a web site). In still othercases, an appropriate system task or response may be to presentinformation to the user in order to elicit from the user additionalinputs that may help the VPA engine 214 clarify the user intent.

Some embodiments of the reasoner 218 may include a dialog managermodule, which keeps track of the current state and flow of eachconversation or dialog that occurs between the user and the VPA 210. Thedialog manager module may apply dialog-managing rules, templates, ortask flows, for example, to the user's NL dialog input that areappropriate for the user's current context. For example, the dialogmanager may apply rules for determining when a conversation has startedor ended, or for determining whether a current input is related to otherinputs, based on one or more of the current or recently-obtainedmulti-modal inputs.

Once the reasoner 218 has determined an appropriate course of action bywhich to respond to the user's inputs, the reasoner 218 communicates an“output intent” to the output generator 220. The output intent specifiesthe type of output that the reasoner 218 believes (e.g., has a highdegree of statistical confidence) is likely appropriate in response tothe user intent, given the results of any business logic that has beenexecuted. For example, if the user intent is “buy product” but thereasoner 218 determines by executing a “check stock” task flow that theproduct the user wants to buy is not available for purchase, the outputintent may be “offer alternative product.”

If the reasoner 218 specifies that the output is to be presented in a(system-generated) natural-language format, a natural-language generatormay be used to generate a natural-language version of the output intent.If the reasoner 218 further determines that spoken natural-language isan appropriate form in which to present the output, a speech synthesizeror text-to-speech (TTS) module may be used to convert natural-languagetext generated by the natural-language generator (or even theun-processed output) to speech (e.g., machine-produced speech using ahuman or humanlike voice). Alternatively or in addition, the systemoutput may include visually-presented material (e.g., text, graphics, orvideo), which may be shown on a display screen of the computing system200, and/or other forms of output.

Each of the components 216, 218, 220 accesses and uses one or more ofthe domain-adapted re-usable VPA components 222. The domain-adaptedre-usable VPA components 222 are versions of the re-usable VPAcomponents 114 that have been adapted for use in connection with aparticular domain that is included in the scope of the functional VPAapplication 210. As such, it should be understood that the re-usable VPAcomponents 114 include any or all of the different types of components222 shown in FIG. 2, although they are not specifically shown in FIG. 1,and/or others. More specifically, the components 222 represent a moredetailed view of the components 114.

The domain-adapted components 222 can be created by applying data from apopulated instance of the domain knowledge base 116 to the re-usable VPAcomponents 114, based on the linkages between the re-usable components114 and the ontology 112 and the linkages between the domain knowledgebase 116 and the ontology 112. For example, data values in the populatedinstance of the domain knowledge base 116 can be mapped to theircorresponding parameters in the re-usable components 114. Thedomain-adapted re-usable VPA components 222 include a number ofdifferent components that “feed” the various executable modules of theVPA engine 214, as described above. In some embodiments, thesecomponents individually or collectively may be referred to as a“models,” “dialog models,” or by other terminology.

The NL grammars 224 include, for example, text phrases and combinationsof text phrases and variables or parameters, which represent variousalternative forms of NL dialog input that the VPA 210 may expect toreceive from the user. As such, the NL grammars 224 help the VPA engine214, the user intent interpreter 216, or more specifically, a rule-based(e.g., PHOENIX) parser, map the user's actual NL dialog input to a userintent. Some examples of NL grammars 224 that may be associated withvarious ontological concepts of an e-commerce ontology 710 are shown inFIG. 7, described below. Some examples of NL grammars 224 that may beassociated with a user intent of “buy product” in a VPA application forthe domain of e-commerce are shown in column 1012 of FIG. 10. Astatistical parser is another mechanism by which the user intentinterpreter 216 (or an NLU portion thereof) may determine user intent.Whereas the rule-based parser uses the NL grammars 224, the statisticalparser uses a statistical model 242 that models different userstatements and determines therefrom (statistically), the most likelyappropriate user intent.

The intents 226 are, as described above, computer-intelligible forms ofthe intended goal of the user's NL dialog input as interpreted by theuser intent interpreter 216. As such, the intents 226 may be derivedfrom other re-usable components 222 (i.e., the grammars 224 andstatistical models 242). The intents 226 help the VPA engine 214 or thereasoner 218, more specifically, determine an appropriate course ofaction in response to the user's NL dialog input. As noted above, theuser intent may be represented as a noun-verb/action combination such as“buy product.” Some examples of intents 226 that may be linked withvarious ontological concepts in the e-commerce ontology 710 are shown inFIG. 8, described below. Referring to FIG. 10, each of the grammarsshown in column 1012 is associated with the “buy product” intent. Inother words, if a user says, “let's go ahead and check out,” anembodiment of the VPA 210 constructed with the VPA builder 1010 of FIG.10 may deduce that the user intends to buy the product, based on theassociation of that NL grammar (or a similar grammar) with the “buyproduct” intent in the ontology 710, and proceed accordingly.

The interpreter flows 228 are devices that help the VPA engine 214 ormore specifically, the user intent interpreter 216, determine theintended meaning or goal of the user's NL dialog inputs. For example,the interpreter flows 228 may include combinations or sequences of NLdialog inputs that, if they occur in temporal proximity, may indicate aparticular user intent.

The task flows 230 (which may be referred to as “business logic,”“workflows” or by other terminology) define actions that the VPA 210 mayperform in response to the user's NL dialog inputs and/or othermulti-modal inputs, or in response to the completion of another taskflow. As such, the task flows may include combinations or sequences offunction calls or procedure calls and parameters or arguments. Someexamples of task flows 230 are illustrated in FIG. 8 and in column 1014of FIG. 10.

The rules 232 may include a number of different rules (e.g., if-thenlogic) that may be applied by the VPA engine 214. For example, a ruleused by the output generator 220 may stipulate that only text or visualoutput is to be generated if the user's NL dialog input is entered astext input as opposed to audio. The NL responses 234 are similar to theNL grammars 224 in that they include, for example, text phrases andcombinations of text phrases and variables or parameters, whichrepresent various alternative forms of possible system-generated NLdialog output that the VPA 210 may present to the user. As such, the NLresponses 234 help the VPA engine 214 or the output generator 220, morespecifically, map the output intent formulated by the reasoner 218 to anappropriate NL dialog output. Some examples of NL responses 234 that maybe associated with various ontological concepts of an e-commerceontology 710 are shown in FIG. 7, described below. Some examples of NLresponses 234 that may be associated with a user intent of “buy product”in a VPA application for the domain of e-commerce are shown in column1016 of FIG. 10. The output templates 236 may be used by the VPA engine214 or more particularly, the output generator 220, to formulate amulti-modal response to the user (e.g., a combination of NL dialog andgraphics or digital images). In some embodiments, the output templates236 map to one or more NL responses 234 and/or output intents.

The acoustic models 238, language models 240, and statistical models 242are additional VPA components that can be defined at a general-purposelevel and then adapted to one or more particular domains through theontology 112. The VPA engine 200 or more specifically, the user intentinterpreter 216 and/or the reasoner 218 may consult one or more of themodels 238, 240, 242 to determine the most likely user input intent asdescribed above. For example, embodiments of the user intent interpreter216 or more specifically, an ASR component, may utilize the acousticmodels 238 to map orally-articulated words or phrases to their textualcounterparts. In some embodiments, a standard high-bandwidth acousticmodel may be adapted to account for particular phraseology or vocabularythat might be specific to a domain. For instance, terminology such as“boot cut” and “acid wash” may have importance to a VPA application thatis directed to the sale of women's jeans but may be meaningless in othercontexts. Thus, a VPA developer may be prompted to include mathematicalrepresentations of the audio speech of those terms in an acoustic model238 by virtue of the inclusion of those terms in a domain knowledge base116 that is linked to the ontology 112 (and to which the acoustic model238 is also linked). Similarly, the language models 240 (which may, forexample, determine whether a sequence of words recognized by an ASRmodule represents a question or a statement) and statistical models 242(described above) may be provided with the VPA development platform 110as re-usable components 114 and adapted for use in connection with aspecific domain.

FIGS. 3-5 illustrate aspects of the ontology populating agent 118 ingreater detail. Referring now to FIG. 3, an embodiment of the ontologypopulating agent 118 includes a semantifier 310, an inference engine312, a local model 314, and a local model creator 316. Using theontology 112, the local model 314, and one or more machine learningmodels 318, the illustrative semantifier 310 adds domain-specificinstances 322 to the domain knowledge base 116, based on the data valuesgleaned from a web page 320 (or another similar electronic data source),in an automated or at least partially automated fashion. As a result,the data values in the domain knowledge base 322 map to the ontologicalconcepts, properties, and relationships of the ontology 112. Thesemantifier 310 analyzes all of the available information about thedomain, e.g., the local model 314, the ontology 112, the machinelearning models 318, and the data values contained on the web page 320itself, and converts that information into a semantic representation ofthe domain knowledge, which is stored in the domain knowledge base 322.As used herein, “semantic representation” or “semantic interpretation”may refer to a structured representation of data that indicates the datavalue itself and the meaning or data type associated with such value.For instance, a semantic representation of “$1.99” may be: “(saleprice=)$1.99,” while a semantic representation of “Jack Jones” may be:“(author name=)Jack-Jones.” In some embodiments, the semanticinterpretation of the data may result in the data being assigned by thesemantifier 310 to certain tables and/or fields of a databaseimplementation of the domain knowledge base 322.

Some embodiments of the semantifier 310 include an inference engine 312.Other embodiments of the semantifier 310 may not include the inferenceengine 312, in which case additional manual effort may be required inorder to perform the semantic interpretation of the web page 320 andpopulate the domain knowledge base 322. The illustrative inferenceengine 312 interfaces with the ontology 112, the local model 314, andthe machine learning models 318 to provide proactive or even “handsfree” semantification of the data contained in the web page 320. Forexample, the inference engine 312 interfaces with the ontology 112 tolearn about the kind of data values it should expect to find in thelocal model 316 and how data values may combine to form compositesemantic objects. As a result, the semantifier 310 can more quicklypopulate the domain knowledge base 116 with data objects that correspondto the concepts, properties and relationships in the ontology 112.

The inference engine 312 interfaces with the local model 314 to learn,for a given web page 320, how to instantiate the ontological entitiesand properties in the knowledge base 322. For example, the inferenceengine 312 may learn from the local model 314 where certain types ofdata elements are located on the particular web page 320 in relation tothe web page as a whole and/or in relation to other data elements. As aresult, the semantifier 310 can more quickly associate or assign thevarious data elements of the web page 320 with the correspondingportions of the ontology 112. The local model 314 is created by thelocal model creator 316, which is described in more detail below withreference to FIG. 4.

The inference engine 312 interfaces with the machine learning models 318to retain learning from previous semantification sessions. The machinelearning models 318 are derived by automated machine learning algorithmsor procedures, such as supervised learning procedures that may beexecuted during the process of creating the local model 314 (FIG. 4).Such algorithms or procedures analyze patterns of data and/or userbehavior that tend to reoccur over time and draw inferences based onthose patterns. In some embodiments, a combination of different machinelearning algorithms may be used. For example, a version space learningalgorithm may be used to locate different data elements on a web pageand/or to learn the appropriate string transformations (i.e.,normalization), while an averaged perception algorithm or other“generic” machine learning procedure may be used to learn the structureof different types of string values to predict the correct ontologicaltype. Any of a number of different machine learning algorithms may beapplied to develop the local model 314, including those mentioned above,decision tree induction, neural networks, support vector machines, naïveBayes, and/or others.

The machine learning models 318 allow the inference engine 312 to retainpreviously-learned associations and data mappings over time. Forexample, over time, the inference engine 312 may come to recognize theweb page 320 represents the web page of a particular e-commerce vendorlike BARNESANDNOBLE.COM after only having been previously exposed to amore “generic” e-commerce web page, for example, a web page forAMAZON.COM, and thereby quickly establish the mapping from the recordlocated on the web page 320 to the ontological concepts and propertiesin the ontology 112. This can enable the semantifier 310 to perform thesemantic interpretation more quickly in future sessions, and ultimately,with little or no human involvement. The bidirectional arrow in FIG. 3(and similarly in FIG. 4) connecting the machine learning models 318with other components indicates two-way data communications. Forexample, in FIG. 3, conclusions made by the semantifier 310 in certaininstances of semantification are used to update the machine learningmodels 318, and vice versa.

The data values contained in the web page 320 can be extracted from theweb page 320 using any suitable web mining or web scraping technique.One example of a web scraping tool is OUTWIT HUB. In general, thesetools identify content (e.g., text, graphics, and/or video) that isdisplayed on Internet web pages and copy the content to a spreadsheet,table, or other electronic file. In some embodiments, a web scrapingtool like OUTWIT HUB may be used to preliminarily scrape the data fromthe web page 320 and provide it to the semantifier 310; however,additional programming would likely be needed to achieve many of thefeatures that are automated by the ontology populating agent 118 asdescribed herein. In other embodiments, the web scraping or web miningmechanism may be incorporated into the semantifier 310, itself (e.g., aspart of the ontology populating agent 118).

The ontology populating agent 118 can be executed one time or multipletimes, as often as needed. For instance, the ontology populating agent118 may be run the first time to populate the domain knowledge base 322in preparation for the creation of a VPA application 210 for a specificdomain. The ontology populating agent 118 may be run subsequently on atimed schedule (e.g., weekly or monthly) or as needed, to update thedata values in the domain knowledge base 322 or to update the localmodel 314 (e.g., in the event the format of the web page 320 or thearrangement of the data thereon changes). For example, in the e-commercecontext, the ontology populating agent 118 may be run more frequentlyduring certain times of the year that are considered “sale” periods(like holidays and back-to-school sale periods), to account for frequentprice changes, changes in product availability, and the like.

Further, the ontology populating agent 118 is applicable acrossdifferent domains, to create domain knowledge bases 322 for multipledifferent domains. For example, if a VPA is being developed for adepartment-store e-commerce vendor, the ontology populating agent 118may be run one time to create a domain knowledge base from a “ladies'shoes” web page and another time to create a domain knowledge base froma “men's shirts” web page. Such knowledge bases for different domainscan be linked with one another through the ontology 112, in someembodiments, as discussed elsewhere herein. In view of the foregoing,references herein to the web page 320 may refer, in various embodiments,to an initial version or instance of a particular web page, to asubsequent version or instance of the same web page, or to an entirelydifferent web page.

An update to the domain knowledge base 322 made by the ontologypopulating agent 118 may trigger a corresponding update to the VPAapplication 210. For example, the ontology populating agent 118 may sendan electronic notification to the VPA developer through the ontologyvisualization module 120 or some other electronic communicationmechanism (e.g., email or text message), whenever the agent 118 hasfinished updating the domain knowledge base 322, to signal that the VPAapplication 210 may need to be updated as well.

Referring now to FIG. 4, an embodiment of the local model creator 316,which creates and updates the local model 314 from time to time for aspecific domain (as defined by the web page 320 or a subsequent versionof it), is shown. The local model creator 316 includes a record locator414, an element classifier 416, a record classifier 418, a valuenormalizer 420, and a user interface 422. The local model creator 316accesses or receives the web page 320 (or a scraped version thereof, asdiscussed above) by electronic transmission over a network 412 (e.g.,the Internet). The local model creator 316 interfaces with the ontology112 and the machine learning models 318 to analyze the web content ofthe web page 320 and locate particular data records and values thereon.The local model creator 316 uses statistical classifiers to formulatehypotheses as to the identification of and location of the differenttypes of content that are present on the web page, and such hypothesesare stored in the local model 314. Further, once content is classified,the local model creator 316 prepares the content for use by the VPAdevelopment platform 110 and/or the ultimately-developed VPA application210 created therefrom.

The illustrative record locator 414 analyzes the web page 320 andattempts to interpret the web page 320 as a list of objects, or asmultiple lists of data objects, where each data object has a set ofcorresponding properties. As used herein, references to “data” or “dataobjects” may refer to any type of electronic content, includingalphanumeric text, graphics, recorded audio or video, and/or others. Therecord locator 414 typically does not determine the semantics associatedwith the data objects and properties from the web page 320, but simplyorganizes the data objects into lists of items that appear to relate tothe same thing, which is considered to be a “record.” For example, therecord locator 414 may create a “pants” list that contains a picture ofa person wearing a pair of pants and the textual elements that appearbelow the picture on the web page 320, but the record locator 414doesn't yet know that the record corresponds to pants. If anotherpicture of another pair of pants appears on the same page with textbelow the picture, the record locator may create another list/record,similarly.

The illustrative element classifier 416 analyzes the data elements ineach of the object lists/records discovered by the record locator 414 onthe web page 320, and attempts to determine therefrom the type of datavalue that each element in the record represents (e.g., by looking atthe string it contains, or by reviewing any associated meta data). To dothis, the element classifier 416 obtains the possible value types fromthe ontology 112. In other words, the element classifier 416 attempts tomap the data elements in each of the object lists to properties that aredefined in the ontology 112, in order to identify the ontologicalconcept associated with those data elements. As such, the local modelcreator 316 typically proceeds in a “bottom-up” fashion to derive theontological concept(s) that are applicable to the web page 320 based onthe properties that are associated with the individual data elements onthe page. The output of the element classifier 416 is typically a set ofpredicted value types, along with confidences (e.g., a computednumerical score that represent how certain the element classifier 416 isabout a particular value type assignment).

For example, if a data value is “$5.99,” and the ontology 112 only hasone interpretation for a price (e.g., a “list price”), the elementclassifier 416 may predict with a high degree of confidence that thedata value “$5.99” corresponds to the value type of “list price.”However, if the ontology has multiple interpretations for a price (e.g.,a “regular price” and a “sale price”), the element classifier 416 maystill predict that the data value $5.99 corresponds to a price (based onthe data including a numerical value preceded by the “$” symbol), butthen may consult the machine learning model 318 to determine whether itis more likely that the data value is a regular price or a sale price.If the element classifier 416 is uncertain, it may make a guess andassign a value type of “regular price” to the data value, but with alower degree of confidence. In some cases, the element classifier 416may prompt the user to review and provide feedback on its guess, via theuser interface 422. In other cases, for example if the confidence valueis very low, the element classifier 416 may simply ask the user toselect the applicable ontological property/value type without firstmaking a guess.

The illustrative record classifier 418 reconciles all of theelement-level value type predictions and user-input assignments of valuetypes/properties and maps the entire record into an ontological objecttype or concept based on an actual or inferred common characteristic.For example, if the element classifier 416 concludes that an object listcontains data values for a size, price, description, color, and inseam,the record classifier 418 may conclude that this particular combinationof data elements likely corresponds to the properties of a “pants”concept of the ontology 112, and assign a confidence value to itsprediction. The record classifier 418 may thus interface with theontology 112 and the machine learning models 318 in a similar manner asdescribed above. The bidirectional arrow between the element classifier416 and the record classifier 418 indicates bidirectional communication.Property or value type assignments that are made by the elementclassifier 416 (or by the user to the element classifier 416 via theuser interface 422) are used by the record classifier 418 to resolve anyambiguities in the classification of the record. Similarly, once therecord classifier 418 has made a prediction as to the proper recordclassification, the record classifier 418 provides that information tothe element classifier 416. Based on the ontology 112 and given therecord/concept classification, the element classifier 416 can then havegreater clarity as to the properties and value types that should beassociated with the record. Here again, the user may input arecord/concept assignment manually through the user interface 422. Suchuser-supplied assignments of properties and concepts may be assumed tohave the same level of confidence as a highly confident assignment madeby the record classifier 418 or element classifier 416, as the case maybe.

For example, a “date” element may have many possible interpretations inthe ontology 112, but its observed co-occurrence with other dataelements on the web page 320 that have already been classified with ahigh degree of confidence by one or more of the classifiers 416, 418 mayprovide a strong indication of its likely appropriate assignment. Forinstance, if other nearby data elements are classified as name, height,and weight, the element classifier 416 may classify the date value as abirth date, which may lead the record classifier 418 to classify therecord as a “person” or “personal health” record, which may then leadthe element classifier 416 to review and re-classify other data elements(for example, to re-classify a data value of “blue” as “eye color”instead of “color”).

The illustrative value normalizer 420 prepares the classified recordsand data values for use by downstream software components. For example,if the VPA application 210 expects numerical and date values to bespelled out, the value normalizer 420 may perform this conversion beforethe data is stored in the domain knowledge base 116. The valuenormalizer 420 similarly transforms other types of data values into theform that may be required by the platform 110 and/or the VPA 210. Asanother example, if the VPA application 210 requires first and lastnames to be separated (e.g., stored in separate fields), the valuenormalizer 420 may perform the task of separating the name data intofirst and last names.

In some cases, the normalization procedure may be demonstrated by theuser via the user interface 422. For instance, the user may provide afew examples of the desired form of the information, and the valuenormalizer 420 may use the machine learning models 318 to acquire thedesired transformation. As with other aspects of the local model creator316, over time and with the help of the machine learning models 318, thevalue normalizer may acquire enough structural knowledge about a valuetype that it can predict the necessary transformations without any userinput. In such cases, the local model creator 316 may displaypredictions to the user via the user interface 422 for verification. Thenormalized data values, element types, and mappings to ontologicalconcepts and properties generated by the local model creator 316 arestored in the local model 314, which is then used by the semantifier 310to convert data newly appearing on the web page 320 (and/or other webpages) into information that is usable by the platform 110 and/or theVPA 210.

In some embodiments, the semantifier 310 and/or other components of theontology populating agent 118 may interface with one or more componentsof the VPA engine 214, such as the user intent interpreter 216. Variouscomponents of the user intent interpreter 216 processes portions of theNL dialog user input during the “live” operation of the VPA 210 and needto figure out what it means and/or what to do with the live input. Forinstance, if a user says to the VPA, “I'll take it for seventy nineninety five,” the VPA 210 needs to determine whether the user means“$79.95” or “$7,995.” Thus, if a component of the VPA engine 210 canmake these determinations and communicate with the ontology populatingagent 118 accordingly, the semantifier's job of normalizing dataobtained from the web page 320 may be simplified.

In some embodiments, the semantifier 310 or another component of theontology populating agent 118 may, through the ontology 112, have accessto certain of the VPA components 114 (such as grammars or userutterances mapped to grammars). Such access may assist the ontologypopulating agent 118 in recognizing data elements or records on the webpage 320, by giving it a supplementary set of features in the form oftypical vocabulary that is associated with various semantic types.

The user interface 422 may be embodied as any suitable type ofhuman-computer interface that allows an end user to review the inputthat is undergoing semantification and the system output (e.g.,classifier predictions), and input data and/or feedback (e.g., mouseclicks, taps, vocal commands, etc.) to the local model creator 316. Forexample, the user interface 422 may include a web browser and/or othersoftware components that interface with one or more of the user inputdevices 1118 discussed below with reference to FIG. 11.

Referring now to FIG. 5, an illustrative method 500 for populating adomain ontology is shown. The method 500 may be embodied as computerizedprograms, routines, logic and/or instructions executed by the computingsystem 100, for example by the ontology populating agent 118 or thesemantifier 310. The method 500 begins with the assumption that thelocal model 314 has already been developed (e.g., by the local modelcreator 316). As such, populating the ontology is now just a matter ofconsulting the local model 314, which tells the system 100 (e.g., thepopulating agent 118) precisely where to look for each record, property,semantic information, etc., on the web page 320. In other words, at thebeginning of the method 500, all of the “hard work” has already beenperformed by the local model creator 316 (to create the local model314), and the system 100, or more specifically the semantifier 310, isin possession of clear instructions for populating the ontology from aparticular web page or web site (by interfacing with the local model314).

At block 510, the system 100 determines whether a triggering event hasoccurred to initiate the process of populating a domain ontology (whichmay be instantiated as a domain knowledge base 116). As discussed above,the triggering event may be, for example, a recent update to a web page320, the expiration of a period of time, the occurrence of a certaindate on the calendar, or user input. If no such event has occurred, thesystem 100 simply remains at block 510 or does nothing.

At block 512, the system 100 reads the source web page 320 (or otherelectronic data source, as mentioned above) and identifies the recordand data values on the page using the local model 314. As noted above,the local model creator 316 has previously learned the locations andvalue types of records and data values on the web page 320 by applying,for example, the record locator 414, the element classifier 416, therecord classifier 418, and the value normalizer 420 to a previousversion of the web page 320 and/or other training data. At block 514,the system 100 uses the local model 314 to determine a likely value typethat corresponds to each of the identified data values. The system 100maps the record, data value, and value type information to the concepts,properties, and relationships defined in the domain ontology (e.g., theontology 112), at block 516.

At block 518, the system 100 determines whether there are any otherrecords on the web page 320 that need to be processed. Such may be thecase if, for example, the web page contains a display of search results,such as a listing of a variety of different purchasable items. If thereis another record to be processed on the web page 320, the system 100returns to block 512 and begins processing the new record as describedabove. If all of the records on the current web page 320 have beenprocessed, the populating of the domain ontology based on the currentweb page concludes and the system 100 proceeds to block 520. At block520, the system 100 determines whether to continue, e.g., to continuepopulating the same domain ontology or begin populating another domainontology. This may be the case if, for example, the end VPA application210 is to be directed to multiple domains (e.g., a department store or“big box” retailer, in the e-commerce context), or if another web pageneeds to be processed to complete the populating of the current domainontology. In such event, the system returns to block 512 and continuesthe ontology populating processes. If no further ontology populating isneeded, the system 100 ends the ontology populating processes untilanother triggering event is detected. The decisions made at blocks 518and 520 may be made by the system 100 based on user inputs at each ofthose steps, or programmatically based on information that is thenavailable to the system 100.

Referring now to FIG. 6, a graphical representation 600 of an embodimentof a hierarchical domain ontology 610 is shown. The ontology 610 may beused in connection with the platform 110, e.g., as a populated versionof the shareable ontology 112, described above (e.g., as an overallontological structure that also has several populated “leaves” or“nodes”). For instance, the semantifier 310 may have already “fed” thestructure or portions thereof with real data values, or the data mayhave been obtained in some other way. In that sense, the ontology 610may be viewed as one illustrative implementation of a domain knowledgebase 116 or a hierarchy of such knowledge bases.

The illustrative ontology 610 is hierarchical in design, in thatontological concepts or entities are arranged according to inheritancerelationships indicated by the connections 660, 662, 664, 666, 668.Illustratively, each of the ontological concepts or entities 612, 618,626, 634, 642, 650 are referred to in FIG. 6 as “domains,” based on theidea that any ontological concept can represent a domain for purposes ofdefining the scope of a particular VPA application 210. On the otherhand, a VPA application 210 may encompass in its scope more than one ofthe “domains” 612, 618, 626, 634, 642, 650, in which case those domainsmay be considered sub-domains of a broader domain. Moreover, in someembodiments, one or more of the domains 612, 618, 626, 634, 642, 650 mayrefer not simply to an ontological concept, but another ontologyaltogether. That is, in some embodiments, the ontology 610 may actuallybe embodied as a hierarchy of (hierarchical) ontologies. Further, forpurposes of the platform 110, the “ontology” 112 may be defined toinclude all or any portion of the hierarchical ontology 610. Forexample, in some embodiments, levels 1 and 2 may be sufficient, while inother embodiments, all of the levels 1 to N (where N is any positiveinteger) may be needed. Additionally, in some embodiments, the ontology112 may be defined to include some “chains” of inheritance or portionsthereof but not others (e.g., the right side 612, 626, 650 but not theleft side 618, 634, 642).

Each of the domains in the ontology 610 includes or has associatedtherewith a number of VPA components and content, which are linked withthe domains based on common characteristics, through computerprogramming statements or other automated or partially mechanisms asdescribed above. Illustratively, the root-level domain 612 defines ageneral purpose or shared ontology that includes or links with defaultVPA components 614 and root-level content 616. For example, if theroot-level domain 612 is “e-commerce,” a default or root-level VPAcomponent 614 might include a generic “buy product” intent or an “add tocart” task flow, while the root-level content 616 may include the namesof high-level product categories (e.g., health care, financial,business) and/or product properties at a high level (e.g., quantity,price). Each of the levels 2 through N below the root level defineincreasingly greater degrees of domain or concept specificity. In otherwords, the root level domain 612 defines the highest degree ofabstraction in the ontology 610. Each of the levels 2 through N caninclude any number of domains or ontological entities. For instance,level 2 may include X number of domains, and level N may include Ynumber of domains, where N, X, and Y may be the same or differentpositive integers.

The root-level VPA components 614 and content 616 are inheritable by thesub-domains 618, 626 by virtue of the inheritance relationships 660,662. As such, the sub-domains 618, 626 include or are linked withinherited VPA components 620, 628, which may include all of theroot-level components 614 by virtue of the subsumption relationship. Inaddition, the domains 618, 626 include or are linked withdomain-specific VPA components 622, 630, respectively, which aretypically domain-adapted versions of the inherited components 620, 628but may also include other customized VPA components that have noapplicability to the root-level domain 612. The level 2 domains 618, 626also include or are linked with content 624, 632, respectively, whichmay include the root-level content 616 by virtue of the subsumptionrelationship, but may also include domain-specific content such asdomain-specific properties. Similarly, the level N domains 634, 642, 650inherit the root level components and content 614, 616, as well as thelevel 2 components 620, 628 and content 624, 632, respectively, but alsomay include or are linked with domain-adapted and/or domain-specific VPAcomponents 638, 646, 654 and content 640, 648, 656, respectively. Inthis way, a developer using the platform 110 to develop a VPAapplication for the domain 634, for example, may automatically haveaccess to the VPA components 614, 622 and therefore not have to createthose components by hand.

The inheritability of the re-usable VPA components 114 is furtherillustrated by FIGS. 7-8. Referring now to FIGS. 7-8, an embodiment of aVPA model, including an e-commerce ontology 710 created using thehierarchical ontology 610, is shown. The illustrative e-commerceontology 610 includes a general purpose ontological concept of“purchasable item” 712, which includes or is linked with a generalpurpose NL grammar 732 and a general purpose NL dialog 740. As can beseen in FIG. 7, these general purpose components 732, 740 includeparameters (<product>, <avg-rating>, <rating type>), which can bereplaced with domain-specific data values.

The NL grammars 734, 736 illustrate domain-specific instances of thegeneral purpose grammar 732, and the NL dialog 742 illustrates adomain-specific instance of the NL dialog 740. The NL grammar 734 isincluded in or linked with the apparel ontological concept 714, whichhas a few properties (style, size, and care) that are not inherited fromthe purchasable item 712. The domain-adapted version 734 of the grammar732 adds the style property and data values corresponding to each of thestyle, product, and color parameters. Thus, once these components arecreated and linked with the ontology 710, a developer working on a VPAapplication 210 for apparel may, using the platform 110, select theapparel 714 concept and automatically have access to both the grammar734 and the grammar 732. Likewise, the grammar 736 represents anotherdomain-specific adaptation of the grammar 732, which may be createdeasily by the developer using the platform 110, by adapting or creatinga new version of either the grammar 732 or the grammar 734 to includethe specific data values for boot-cut jeans in acid-washed indigo. Thedialog 742 may be created by a developer using the platform 110 in asimilar manner, by adapting or creating a new version of the dialog 740that includes the specific data values for smart phones.

It should be appreciated that the illustrations of FIGS. 7-8 and 10 arehighly simplified. Referring to FIG. 7, only a single phrase that mapsto the input intent “product search,” is illustrated. However, there aretypically numerous (e.g., in the thousands or more) of different waysthat humans express themselves in NL dialog that all map to the sameintent, “product search.” Thus, the developer working on the VPAapplication for apparel 714 using the platform 110 has an enormous headstart by having automated access to these many thousands of variationsof grammars 734 that have already been successfully used in other(pre-existing) domains.

Other types of VPA components 114 can be made re-usable through theinheritance relationships of the ontology 710, as shown in FIG. 8. InFIG. 8, the intent 810 and the task flow 814 represent default re-usableVPA components 114 while the intent 812 and task flow 816 representdomain-adapted versions of the same. Thus, a developer creating a VPA210 for either apparel or electronics may, using the platform 110,automatically have access to the intent 810 and the task flow 814, dueto the inheritance relationships between each of those concepts 714, 726and the purchasable item 712. As above, the illustration of task flowsin FIG. 8 is highly simplified. In practice, there are many such taskflows, beyond just the ones illustrated here, and all of those taskflows would be made available to the developer in an automated fashionby virtue of the linkages with the ontology 710.

Referring now to FIG. 9, an illustrative method 900 for developing a VPAapplication using the tools and techniques disclosed herein is shown.The method 900 may be embodied as computerized programs, routines, logicand/or instructions that may be executed by the computing system 100, bythe VPA development platform 110, for example. At block 910, the system100 determines a domain of interest for the development of the VPAapplication. The domain of interest may be obtained though interactionwith the user via the ontology visualization module 120, or by othermeans. For example, the developer may simply click on or otherwiseselect a graphical element corresponding to the domain of interest on atree-like depiction of the ontology 112, such as is shown in FIGS. 6-8.

At block 912, the system 100 selects the re-usable VPA components 114from the VPA developer's toolkit (e.g., the platform 110), and anypertinent related components, based on the ontology 112 (block 914) andthe domain of interest. To do this, the system 100 may present agraphical user interface such as is shown in FIG. 10, and may allow thedeveloper to select all of the VPA components 114 associated with aparticular ontological concept simply by clicking on or otherwiseselecting the desired concept in the tree-like depiction of the ontology112. Referring to block 912, if the developer selects a single VPAcomponent, the system 100 may automatically offer or select all of thecomponents that are related to the selected component through theontology 112.

At block 916, the system 100 determines whether any of the selectedcomponents need to be customized for the particular domain of interest.To do this, the system 100 may allow the developer to interactivelyselect components to be customized. In some cases, the system 100 mayautomatically determine that certain components 114 need to becustomized based on the hierarchical relationships in the ontology 112or the fact that the domain of interest is not yet represented in theontology 112, for example. If no components 114 need to be customized,the system 100 skips block 918.

If one or more of the components needs to be customized, the system 100creates the customized versions of the components for the domain ofinterest, as needed, at block 918. To do this, the system may provide auser interface by which the developer can create new VPA components byhand, or create new domain-adapted versions of the components 114 thatincorporate data values from the domain knowledge base 116. In somecases, data values from the knowledge base 116 may be automaticallymerged by the system 100 with the re-usable components 114 thatreference the corresponding parameters, based on the data relationshipsand linkages connecting the elements of the domain knowledge base 116with the ontology 112 and those connecting the re-usable components 114with the ontology 112. For example, color, size and style data stored ina portion of the domain knowledge base 116 that includes data values forwomen's jeans may be automatically linked or merged with VPA components114 that are connected with the jeans 716 entity of the ontology 710, tocreate VPA components that are adapted to the jeans domain.

Following block 916 or block 918, as the case may be, the systemproceeds to create the VPA application with the domain-adapted re-usablecomponents and any newly created customized components, at block 920. Todo this, standard programming mechanisms may be used to compile,assemble and/or link the various components of the VPA 210, as should beunderstood by those skilled in the art. At block 922, the system 100updates the VPA developer's toolkit (e.g., the platform 110) to includethe new domain-adapted and/or customized VPA components, as needed, sothat they may be re-used in future VPA developments.

At block 924, the system 100 determines whether to continue building theVPA application 210 by adding another domain to the VPA. For example, ane-commerce VPA for a department store may need to ultimately includere-usable components for both apparel 714 and electronics 726. The VPAdeveloper may thus choose to develop and test the VPA for one of thosedomains and then add the other later. In other embodiments, multipledifferent domains may be added to the VPA application before thefunctional version of the application is created at block 920. Ifanother domain is to be added to the VPA 210, the system 100 returns toblock 910. If no additional domains are to be added to the VPA 210, thesystem 100 concludes its execution of the method 900.

Referring now to FIG. 10, an illustrative graphical user interface 1000for the VPA development platform 110 is shown. An interactive displayscreen 1010 is shown in response to the developer's selection of ane-commerce domain and an intent of “buy product.” Columns 1012, 1014,106 display re-usable VPA components 114 (e.g., NL grammars, task flows,and NL responses) that are linked with the “buy product” intent. All ofthese components 114 are automatically displayed and selected by virtueof their relationship with the “buy product” intent through the ontology112. The platform 110 allows the developer to de-select one or more ofthe displayed components via the graphical user controls 1018, 1020,1022, or by simply de-selecting individual components displayed in thecolumns 1012, 1014, 1016. Once the desired components have beenselected, the developer may select the “go” control 1024 to proceed withthe next step of VPA creation. As with FIGS. 7-8, described above, FIG.10 represents a highly simplified depiction of a user interface in thatthere are typically a very large number of NL grammars, task flows, andresponses associated with each intent, as well as a large number ofintents, associated with a given VPA domain.

Example Usage Scenarios

Embodiments of the VPA development platform 110 and/or portions thereofcan be used to accelerate the deployment of VPA applications in domainsthat have large data sets, such as e-commerce, geographic navigation andsurveillance (e.g., maritime or aviation traffic monitoringapplications), and/or others. Embodiments of the VPA developmentplatform 110 may be used to facilitate the maintenance and support ofVPA applications by automating the process of updating the VPAcomponents as information in the application domain changes. Byproviding re-usable VPA components, embodiments of the VPA developmentplatform 110 can make it easier to develop new VPA applications or addnew domains to an existing VPA application.

For example, in the domain of e-commerce, a developer may be engaged todevelop a VPA application for a ‘boutique’ vendor such as a vendor ofhigh-end jewelry or handbags, or designer clothing. Using an embodimentof the ontology populating agent 118, the developer may quickly obtain alarge amount of detailed information about the vendor's products frompublicly accessible sources (e.g., Internet web pages), and organize itin a way that can be readily accepted by the components of the VPAapplication or used to create customized versions of previously-created“default” e-commerce VPA components. When the product informationchanges, embodiments of the ontology populating agent 118 can be used toupdate the ontology 112 and thereby facilitate incorporation of theinformation updates into the VPA components in an automated way.

As another example, a developer may be engaged to create a VPAapplication for a vendor that sells a large assortment of differenttypes of products, such as a department store or “big box” retailer. Inthis scenario, the developer may use an embodiment of the VPAdevelopment platform 110 to create the VPA application for one of theproduct types, initially, based on a generalized ‘retail product’ontology, and then expand the VPA application to include other producttypes in future releases.

Implementation Examples

Referring now to FIG. 11, a simplified block diagram of an exemplaryhardware environment 1100 for the computing system 100, in which the VPAdevelopment platform 110 may be embodied, and/or the computing system200, in which the VPA application 210 may be embodied, is shown. Theillustrative environment 1100 includes a computing device 1110, whichmay be in communication with one or more other computing devices 1132via one or more networks 1130. Illustratively, a portion 110A of the VPAdevelopment platform 110 and a portion 210A of the VPA 210 are local tothe computing device 1110, while other portions 110B, 210B aredistributed across one or more of the other computing systems or devices1132 that are connected to the network(s) 1130.

For example, in some embodiments, portions of the VPA developmentplatform 110 and/or the VPA 210 may be stored locally while otherportions thereof are distributed across a network (and likewise forother components of the VPA development platform 110). In someembodiments, however, the VPA development platform 110 and/or the VPA210 may be located entirely on the computing device 1110. In someembodiments, portions of the VPA development platform 110 and/or the VPA210 may be incorporated into other systems or interactive softwareapplications. Such applications or systems may include, for example,operating systems, middleware or framework (e.g., applicationprogramming interface or API) software, and/or user-level applicationssoftware (e.g., another interactive software application, such as asearch engine, web browser or web site application, or a user interfacefor a computing device).

For ease of illustration, FIG. 11 shows both the VPA developmentplatform 110 and the VPA 210 as being coupled to the I/O subsystem 1116.It should be understood, however, that the VPA development platform 110and the VPA 210 may be located on or connected to the same computingdevice, or entirely different computing devices. For example, in someembodiments, all or portions of the VPA development platform 110 mayreside on a desktop computer, while all or portions of the VPA 210 maybe located on a mobile computing device.

The illustrative computing device 1110 includes at least one processor1112 (e.g. a controller, microprocessor, microcontroller, digital signalprocessor, etc.), memory 1114, and an input/output (I/O) subsystem 1116.The computing device 1110 may be embodied as any type of computingdevice such as a personal computer or mobile device (e.g., desktop,laptop, tablet, smart phone, body-mounted device, etc.), a server, anenterprise computer system, a network of computers, a combination ofcomputers and other electronic devices, or other electronic devices.Although not specifically shown, it should be understood that the I/Osubsystem 1116 typically includes, among other things, an I/Ocontroller, a memory controller, and one or more I/O ports. Theprocessor 1112 and the I/O subsystem 1116 are communicatively coupled tothe memory 1114. The memory 1114 may be embodied as any type of suitablecomputer memory device (e.g., volatile memory such as various forms ofrandom access memory).

The I/O subsystem 1116 is communicatively coupled to a number ofcomponents including one or more user input devices 1118 (e.g., amicrophone, a touchscreen, keyboard, virtual keypad, etc.), one or morestorage media 1120, one or more output devices 1120 (e.g., audiospeakers, displays, LEDs, etc.), one or more sensing devices 1122 (e.g.,motion sensors, pressure sensors, kinetic sensors, temperature sensors,biometric sensors, and/or others), and one or more communicationsinterfaces 1124. The storage media 1120 may include one or more harddrives or other suitable persistent data storage devices (e.g., flashmemory, memory cards, memory sticks, and/or others). Illustratively,portions of the shareable ontology 112A, the re-usable VPA components114A and the domain knowledge base 116A reside in the storage media 1120while other portions 112B, 114B, 116B reside in storage media of theother computing devices 1132. In other embodiments, one or more of thesecomponents may reside entirely on the computing device 1110 or onanother computing device 1132. In some embodiments, portions of systemssoftware (e.g., an operating system, etc.), framework/middleware (e.g.,APIs, object libraries, etc.), the VPA development platform 110, and/orthe VPA 210 reside at least temporarily in the storage media 1120.Portions of systems software, framework/middleware, the VPA developmentplatform 110 (including the ontology 112, components 114, and knowledgebase 116) and/or the VPA 210 may be copied to the memory 1114 duringoperation of the computing device 1110, for faster processing or otherreasons.

The one or more communications interfaces 1124 may communicativelycouple the computing device 1110 to a local area network, wide areanetwork, personal cloud, enterprise cloud, public cloud, and/or theInternet, for example. Accordingly, the network interfaces 1130 mayinclude one or more wired or wireless network interface cards oradapters, for example, as may be needed pursuant to the specificationsand/or design of the particular computing system 100, 200. The othercomputing device(s) 1132 may be embodied as any suitable type ofcomputing system or device such as any of the aforementioned types ofdevices or other electronic devices or systems. For example, in someembodiments, the other computing devices 1132 may include one or moreserver computers used to store portions of the shareable ontology 112,the re-usable components 114A, and/or the domain knowledge base 116. Thecomputing system 100 may include other components, sub-components, anddevices not illustrated in FIG. 11 for clarity of the description. Ingeneral, the components of the computing systems 100, 200 arecommunicatively coupled as shown in FIG. 11 by electronic signal paths,which may be embodied as any type of wired or wireless signal pathscapable of facilitating communication between the respective devices andcomponents.

Additional Examples

According to at least one aspect of this disclosure, a method forpopulating an ontology with content for use in creating a virtualpersonal assistant (“VPA”) computer application for a domain of interestis provided. The VPA application is executable to determine a likelyintended goal of a computing device user based on conversational naturallanguage input of the computing device user, execute a task on behalf ofthe computing device user, and formulate a likely appropriate systemresponse to the conversational natural language input. The methodincludes, with a computing system, obtaining the content in an automatedfashion from one or more Internet web pages that support electronicinteractions with computing device users relating to items in the domainof interest, and analyzing the content using the ontology. The ontologydefines a computerized structure for representing knowledge relating toone or more domains. Each domain refers to a category of informationand/or activities in relation to which the VPA computer application mayconduct a conversational natural language dialog with a computing deviceuser. The method also includes determining a characteristic that thecontent has in common with at least a portion of the ontology, anddefining a data relationship between the content and the ontology basedon the common characteristic.

Any of the methods may include accessing a component of the VPA computerapplication that is linked with the ontology and using the accessedcomponent to recognize the characteristic that the content has in commonwith the ontology. Any of the methods may include identifying acomponent of the VPA computer application that is linked with theontology based on the data relationship between the content and theontology, and using the identified VPA component to analyze the content.Any of the methods may include formulating the content in accordancewith a requirement of a component of the VPA computer application thatis linked with the ontology. Any of the methods may include obtainingthe content from the one or more Internet web sites periodically inresponse to one or more triggering events. Any of the methods mayinclude predicting the common characteristic and/or the datarelationship using a computerized machine learning model. Any of themethods may include repeating the method for another domain of interestto configure the VPA computer application to operate over multipledomains of interest. Any of the methods may include obtaining thecontent from a plurality of different Internet web pages. Any of themethods may include presenting an interactive visualization of theontology on a display of the computing system.

According to at least one aspect of this disclosure, anontology-populating agent is embodied in one or more machine readablestorage media and includes instructions that in response to beingexecuted result in the computing system performing any of the foregoingmethods.

According to at least one aspect of this disclosure, a method forcreating a virtual personal assistant (“VPA”) computer application for adomain of interest is provided. The method includes, with a computingsystem, determining the domain of interest, and accessing a computerizedontology. The ontology defines a structure for representing knowledgerelating to a plurality of domains. Each domain refers to a category ofinformation and/or activities in relation to which the VPA computerapplication may conduct a conversational natural language dialog with acomputing device user. The ontology has linked thereto a plurality ofre-usable VPA components. Each of the re-usable VPA components isaccessible by an executable VPA engine to determine a likely intendedgoal of the computing device user based on a determined meaning ofconversational natural language input of the computing device user,execute a task on behalf of the computing device user, and/or generate alikely appropriate system output in response to the conversationalnatural language input. The method also includes determining a datarelationship between the domain of interest and at least a portion ofthe ontology, and suggesting a re-usable VPA component to use to createthe VPA computer application for the domain of interest based on thedata relationship between the domain of interest and the ontology.

The ontology may include a hierarchical structure of ontologicalconcepts each representing a portion of the knowledge relating to theplurality of domains, where the re-usable VPA component is linked withone of the ontological concepts in the hierarchical structure, the datarelationship between the domain of interest and the ontology includes adata relationship between the domain of interest and another of theontological concepts in the hierarchical structure, and the methodincludes identifying the re-usable VPA component in an automated fashionbased on an inheritance relationship between the one of the ontologicalconcepts and the other of the ontological concepts in the hierarchicalstructure.

Any of the methods may include creating a customized version of thesuggested re-usable VPA component for the domain of interest. Any of themethods may include replacing a parameter of the suggested re-usable VPAcomponent with content associated with the domain of interest. Any ofthe methods may include updating the ontology to include a link betweenthe ontology and the customized version of the suggested re-usable VPAcomponent. Any of the methods may include suggesting another re-usableVPA component to use to create the VPA computer application for thedomain of interest based on the link between the re-usable VPA componentand the ontology and a link between the ontology and the other re-usableVPA component. The suggested re-usable VPA component may include adefault task flow executable by the VPA computer application in responseto the conversational natural language input, and the method may includemapping data from an Internet web page that supports electronictransactions with computing device users relating to the domain ofinterest to the default task flow using the ontology. The suggestedre-usable VPA component may include a natural language dialog componentusable by the VPA computer application to determine the likely intendedmeaning of the conversational natural language input, and the method mayinclude mapping data from an Internet web page that supports electronictransactions with computing device users relating to the domain ofinterest to the natural language dialog component through the ontology.The suggested re-usable VPA component may include a natural languagedialog component usable by the VPA computer application to generate thelikely appropriate system output to the conversational natural languageinput, and the method may include mapping data from an Internet web pagethat supports electronic transactions with computing device usersrelating to the domain of interest to the natural language dialogcomponent using the ontology.

General Considerations

In the foregoing description, numerous specific details, examples, andscenarios are set forth in order to provide a more thoroughunderstanding of the present disclosure. It will be appreciated,however, that embodiments of the disclosure may be practiced withoutsuch specific details. Further, such examples and scenarios are providedfor illustration, and are not intended to limit the disclosure in anyway. Those of ordinary skill in the art, with the included descriptions,should be able to implement appropriate functionality without undueexperimentation.

References in the specification to “an embodiment,” etc., indicate thatthe embodiment described may include a particular feature, structure, orcharacteristic, but every embodiment may not necessarily include theparticular feature, structure, or characteristic. Such phrases are notnecessarily referring to the same embodiment. Further, when a particularfeature, structure, or characteristic is described in connection with anembodiment, it is believed to be within the knowledge of one skilled inthe art to effect such feature, structure, or characteristic inconnection with other embodiments whether or not explicitly indicated.

Embodiments in accordance with the disclosure may be implemented inhardware, firmware, software, or any combination thereof. Embodimentsmay also be implemented as instructions stored using one or moremachine-readable media, which may be read and executed by one or moreprocessors. A machine-readable medium may include any mechanism forstoring or transmitting information in a form readable by a machine(e.g., a computing device or a “virtual machine” running on one or morecomputing devices). For example, a machine-readable medium may includeany suitable form of volatile or non-volatile memory.

Modules, data structures, and the like defined herein are defined assuch for ease of discussion, and are not intended to imply that anyspecific implementation details are required. For example, any of thedescribed modules and/or data structures may be combined or divided intosub-modules, sub-processes or other units of computer code or data asmay be required by a particular design or implementation.

In the drawings, specific arrangements or orderings of schematicelements may be shown for ease of description. However, the specificordering or arrangement of such elements is not meant to imply that aparticular order or sequence of processing, or separation of processes,is required in all embodiments. In general, schematic elements used torepresent instruction blocks or modules may be implemented using anysuitable form of machine-readable instruction, and each such instructionmay be implemented using any suitable programming language, library,application-programming interface (API), and/or other softwaredevelopment tools or frameworks. Similarly, schematic elements used torepresent data or information may be implemented using any suitableelectronic arrangement or data structure. Further, some connections,relationships or associations between elements may be simplified or notshown in the drawings so as not to obscure the disclosure.

This disclosure is to be considered as exemplary and not restrictive incharacter, and all changes and modifications that come within the spiritof the disclosure are desired to be protected.

1. A method for populating an ontology with content for use in creatinga virtual personal assistant (“VPA”) computer application for a domainof interest, the VPA application executable to determine a likelyintended goal of a computing device user based on conversational naturallanguage input of the computing device user, execute a task on behalf ofthe computing device user, and formulate a likely appropriate systemresponse to the conversational natural language input, the methodcomprising, with a computing system: obtaining the content in anautomated fashion from one or more Internet web pages that supportelectronic interactions with computing device users relating to items inthe domain of interest; analyzing the content using the ontology, theontology defining a computerized structure for representing knowledgerelating to one or more domains, each domain referring to a category ofinformation and/or activities in relation to which the VPA computerapplication may conduct a conversational natural language dialog with acomputing device user; determining a characteristic that the content hasin common with at least a portion of the ontology; and defining a datarelationship between the content and the ontology based on the commoncharacteristic.
 2. The method of claim 1, comprising accessing acomponent of the VPA computer application that is linked with theontology and using the accessed component to recognize thecharacteristic that the content has in common with the ontology.
 3. Themethod of claim 1, comprising identifying a component of the VPAcomputer application that is linked with the ontology based on the datarelationship between the content and the ontology, and using theidentified VPA component to analyze the content.
 4. The method of claim1, comprising formulating the content in accordance with a requirementof a component of the VPA computer application that is linked with theontology.
 5. The method of claim 1, comprising obtaining the contentfrom the one or more Internet web sites periodically in response to oneor more triggering events.
 6. The method of claim 1, comprisingpredicting the common characteristic and/or the data relationship usinga computerized machine learning model.
 7. The method of claim 1,comprising repeating the method for another domain of interest toconfigure the VPA computer application to operate over multiple domainsof interest.
 8. The method of claim 1, comprising obtaining the contentfrom a plurality of different Internet web pages.
 9. The method of claim1, comprising presenting an interactive visualization of the ontology ona display of the computing system.
 10. An ontology-populating agentembodied in one or more machine readable storage media and comprising aplurality of instructions that in response to being executed result inthe computing system performing the method of claim
 1. 11. A method forcreating a virtual personal assistant (“VPA”) computer application for adomain of interest, the method comprising, with a computing system:determining the domain of interest; accessing a computerized ontologydefining a structure for representing knowledge relating to a pluralityof domains, each domain referring to a category of information and/oractivities in relation to which the VPA computer application may conducta conversational natural language dialog with a computing device user,the ontology having linked thereto a plurality of re-usable VPAcomponents, each of the re-usable VPA components being accessible by anexecutable VPA engine to determine a likely intended goal of thecomputing device user based on a determined meaning of conversationalnatural language input of the computing device user, execute a task onbehalf of the computing device user, and/or generate a likelyappropriate system output in response to the conversational naturallanguage input; determining a data relationship between the domain ofinterest and at least a portion of the ontology; and suggesting are-usable VPA component to use to create the VPA computer applicationfor the domain of interest based on the data relationship between thedomain of interest and the ontology.
 12. The method of claim 11, whereinthe ontology comprises a hierarchical structure of ontological conceptseach representing a portion of the knowledge relating to the pluralityof domains, the re-usable VPA component is linked with one of theontological concepts in the hierarchical structure, the datarelationship between the domain of interest and the ontology comprises adata relationship between the domain of interest and another of theontological concepts in the hierarchical structure, and the methodcomprises identifying the re-usable VPA component in an automatedfashion based on an inheritance relationship between the one of theontological concepts and the other of the ontological concepts in thehierarchical structure.
 13. The method of claim 11, comprising creatinga customized version of the suggested re-usable VPA component for thedomain of interest.
 14. The method of claim 13, comprising replacing aparameter of the suggested re-usable VPA component with contentassociated with the domain of interest.
 15. The method of claim 13,comprising updating the ontology to include a link between the ontologyand the customized version of the suggested re-usable VPA component. 16.The method of claim 11, comprising suggesting another re-usable VPAcomponent to use to create the VPA computer application for the domainof interest based on the link between the re-usable VPA component andthe ontology and a link between the ontology and the other re-usable VPAcomponent.
 17. The method of claim 11, wherein the suggested re-usableVPA component comprises a default task flow executable by the VPAcomputer application in response to the conversational natural languageinput, and the method comprises mapping data from an Internet web pagethat supports electronic transactions with computing device usersrelating to the domain of interest to the default task flow using theontology.
 18. The method of claim 11, wherein the suggested re-usableVPA component comprises a natural language dialog component usable bythe VPA computer application to determine the likely intended meaning ofthe conversational natural language input, and the method comprisesmapping data from an Internet web page that supports electronictransactions with computing device users relating to the domain ofinterest to the natural language dialog component through the ontology.19. The method of claim 11, wherein the suggested re-usable VPAcomponent comprises a natural language dialog component usable by theVPA computer application to generate the likely appropriate systemoutput to the conversational natural language input, and the methodcomprises mapping data from an Internet web page that supportselectronic transactions with computing device users relating to thedomain of interest to the natural language dialog component using theontology.